Calculating a maturity level of a text string

ABSTRACT

Techniques for determining a maturity level of a text string may be provided. For example, the text string may be associated with an electronic book or story, script or closed captioning of a movie or television show, or other text associated with media. The system may analyze the text string to identify actions in the text (e.g., concepts of death, degree of violence, etc.), story complexity (e.g., number of characters, linear/non-linear story flow, etc.), vocabulary (e.g., unique words, complexity of terms, etc.), and other metrics to determine the objective maturity level associated with the text string. The maturity level and/or references to electronic books associated with the particular maturity level may be provided to users and/or used in additional processing and analysis.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional of and claims priority to U.S. patentapplication Ser. No. 14/557,237, filed Dec. 1, 2014, now U.S. Pat. No.9,672,203 issued on Jun. 6, 2017, and entitled “Calculating a MaturityLevel of a Text String,” which is incorporated herein by reference forall purposes.

BACKGROUND

Web pages provide various options to search for items. For example, auser looking for a book entitled “My Favorite Book” or a movie entitled“My Favorite Movie” may type the title of the book or movie into asearch tool provided by the web page and receive a link to theparticular book or movie the user requested. However, it may be moredifficult to search for content, including the content included withelectronic books or movies. For example, some movies are provided withcontent ratings, but the rating system is subjective, manual, andtime-consuming. This process can be especially troublesome when thenumber of items with content increases too quickly for the subjectiverating system to review and rate in an efficient manner.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments in accordance with the present disclosure will bedescribed with reference to the drawings, in which:

FIG. 1 illustrates an illustrative flow for determining a maturity levelof a text string described herein, according to at least one example;

FIG. 2 illustrates an example architecture for determining a maturitylevel of a text string described herein that includes a content analysiscomputer and/or a user device connected via one or more networks,according to at least one example;

FIG. 3 illustrates an example flow diagram for determining a maturitylevel of a text string described herein, according to at least oneexample;

FIG. 4 illustrates some examples of determining a maturity level of atext string described herein, according to at least one example;

FIG. 5 illustrates some examples of determining a maturity level of atext string described herein, according to at least one example;

FIG. 6 illustrates some examples of determining a maturity level of atext string described herein, according to at least one example;

FIG. 7 illustrates an illustrative flow for providing information to auser based in part on the determined maturity level described herein,according to at least one example; and

FIG. 8 illustrates an environment in which various embodiments ofdetermining a maturity level of a text string can be implemented,according to at least one example.

DETAILED DESCRIPTION

In the following description, various embodiments will be described. Forpurposes of explanation, specific configurations and details are setforth in order to provide a thorough understanding of the embodiments.However, it will also be apparent to one skilled in the art that theembodiments may be practiced without the specific details. Furthermore,well-known features may be omitted or simplified in order not to obscurethe embodiment being described.

Embodiments of the present disclosure are directed to, among otherthings, a system for determining a maturity level of a text string(e.g., two or more linguistic terms that are placed together). Forexample, the text string may be associated with content of an electronicbook or story, script or closed captioning of a movie or televisionshow, or other text associated with media. The system may analyze thetext string to identify linguistic elements and/or actions in the text(e.g., concepts of death, degree of violence, etc.), story complexity(e.g., number of characters, linear/non-linear story flow, etc.),vocabulary (e.g., unique linguistic terms or words, complexity of terms,etc.), and other metrics to determine the objective maturity levelassociated with the text string. The maturity level and/or references toelectronic books associated with the particular maturity level may beprovided to users and/or used in additional processing and analysis.

In an illustrative example, a webpage provides an electronic searchengine (e.g., an electronic marketplace, item search tool, etc.) tosearch for books. A parent uses the web page to search for a book toorder for his child in the first grade. When the user attempts to searchfor a book, the webpage provides several search options for the parent,including a text field (e.g., to provide the title, author, or generictopic of a book) and sort features (e.g., sort by price, etc.). Thewebpage also provides a search option that allows the parent to filterthe search results to books of a certain maturity level (e.g., firstgrade, six years old, sixth year non-native English speaker, etc.). Thiscan help the parent ensure that the ordered book is directed to anappropriate story complexity for the child.

FIG. 1 illustrates an illustrative flow for determining a maturity levelof a text string described herein, according to at least one example.The process 100 can begin with determining at least a portion of a textstring at 102. For example, a computer system 104 can interact with adata store 106 to access information and/or interact with an electronicbook 108 to determine the information. For example, the electronic book108 may contain a text string and the computer system 104 may store thattext string in the data store 106. Other types of information may bestored with the data store 106 without diverting from the scope of thedisclosure.

The process 100 may also generate a character graph for the text stringat 120. For example, the computer system 104 can interact with datastore 106 and/or electronic book 108 to identify nouns or pronouns(e.g., reference to a specific object, including character names “Bob”or “Sally” or replacements for the names “him” or “them”) and/or verbs(e.g., the action, occurrence, or state of being) in the electronic textthrough various techniques, including natural language processing (NLP).As illustrated, the noun or pronoun may include character names. Forexample, a first noun 122A may refer to the character “Bob” and a secondnoun 122B may refer to the character “Jane.” Other characters may bereferenced as well, including characters John and Sally (hereinafter“nouns 122”).

One or more verbs may be included with the character graph as well. Forexample, a first verb 124A may refer to the action “speaks” and a secondverb 124B may refer to the action “talks” (e.g., verb) or “talks to”(e.g., verb and preposition). Other verbs/actions may be referenced aswell, including actions “runs with” and “throws a ball to” (hereinafter“verbs 124”). In an example illustration, the text string may includeone or more nouns, pronouns, and verbs: “John talks to Sally. He speakswith her.” It should be appreciated that evaluating a text string mayinvolve reviewing not only nouns, pronouns, and verbs but alsoadditional terms (e.g., adjectives, adverbs, conjunctions, etc.) withoutdiverting from the scope of the disclosure.

In some examples, the character graph may comprise one or morelinguistic elements. For example, the computer system 104 can interactwith data store 106 and/or electronic book 108 to identify the one ormore linguistic elements. Linguistic elements may include, for example,nouns, verbs, prepositions, pronouns, adverbs, adjectives, simplepresent, simple past, past/present participle, infinitive, title,auxiliary verbs, direct/indirect objects, subject complements, gerund,or other examples of linguistic elements. The linguistic element mayalso include parts of one or more terms (e.g., Greek or Latin rootwords, “ling” or “lang,” ending of a word, etc.), less than an entireword, origin language elements, or conjugations of the terms (e.g.,active/passive, “-ing,” “-ed,” etc.).

The process 100 may also determine a maturity level at 130. For example,the computer system 104 can determine the maturity level from thecharacter graph, electronic book, and/or other analysis of the textstring (e.g., without generating a character graph). The analysis mayinclude identifying actions in the text, story complexity, vocabulary,and other metrics to determine the objective maturity level associatedwith the text string. In some examples, the computer system 104 canprovide a communication through a network page 132 or other application(e.g., as defined in FIG. 2) to the user computing device 134 regardingthe maturity level.

FIG. 2 illustrates an example architecture for determining a maturitylevel of a text string described herein that includes a content analysiscomputer and/or a user device connected via one or more networks,according to at least one example. In architecture 200, one or moreusers 202 (i.e., web browser users) may utilize user computing devices204(1)-(N) (collectively, user devices 204) to access an application 206(e.g., a web browser), via one or more networks 208. In some aspects,the application 206 may be hosted, managed, and/or provided by acomputing resources service or service provider, such as by utilizingone or more service provider computers and/or one or more contentanalysis computers 210. The one or more content analysis computers 210may, in some examples, provide computing resources such as, but notlimited to, client entities, low latency data storage, durable datastorage, data access, management, virtualization, cloud-based softwaresolutions, electronic content performance management, etc. The one ormore content analysis computers 210 may also be operable to provide webhosting, computer application development, and/or implementationplatforms, combinations of the foregoing, or the like to the one or moreusers 202. The one or more content analysis computers 210, in someexamples, may help determine a maturity level of a text string and/orprovide a reference to a text string (e.g., as a recommendation for anelectronic book, movie, etc.) to one or more user devices 204.

In some examples, the networks 208 may include any one or a combinationof many different types of networks, such as cable networks, theInternet, wireless networks, cellular networks and other private and/orpublic networks. While the illustrated example represents the users 202accessing the application 206 over the networks 208, the describedtechniques may equally apply in instances where the users 202 interactwith the content analysis computers 210 via the one or more user devices204 over a landline phone, via a kiosk, or in any other manner. It isalso noted that the described techniques may apply in otherclient/server arrangements (e.g., set-top boxes, etc.), as well as innon-client/server arrangements (e.g., locally stored applications,etc.).

As described briefly above, the application 206 may allow the users 202to interact with a service provider computer, such as to access webcontent (e.g., web pages, music, video, etc.). The one or more contentanalysis computers 210, perhaps arranged in a cluster of servers or as aserver farm, may host the application 206 and/or cloud-based softwareservices. Other server architectures may also be used to host theapplication 206. The application 206 may be capable of handling requestsfrom many users 202 and serving, in response, various item web pages.The application 206 can provide any type of website that supports userinteraction, including social networking sites, online retailers,informational sites, blog sites, search engine sites, news andentertainment sites, and so forth. As discussed above, the describedtechniques can similarly be implemented outside of the application 206,such as with other applications running on the user devices 204.

The user devices 204 may be any type of computing device such as, butnot limited to, a mobile phone, a smart phone, a personal digitalassistant (PDA), a laptop computer, a desktop computer, a thin-clientdevice, a tablet PC, an electronic book (e-book) reader, etc. In someexamples, the user devices 204 may be in communication with the contentanalysis computers 210 via the networks 208, or via other networkconnections. Additionally, the user devices 204 may be part of thedistributed system managed by, controlled by, or otherwise part of thecontent analysis computers 210 (e.g., a console device integrated withthe content analysis computers 210).

In one illustrative configuration, the user devices 204 may include atleast one memory 214 and one or more processing units (or processor(s))216. The processor(s) 216 may be implemented as appropriate in hardware,computer-executable instructions, firmware, or combinations thereof.Computer-executable instruction or firmware implementations of theprocessor(s) 216 may include computer-executable or machine-executableinstructions written in any suitable programming language to perform thevarious functions described. The user devices 204 may also includegeo-location devices (e.g., a global positioning system (GPS) device orthe like) for providing and/or recording geographic location informationassociated with the user devices 204.

The memory 214 may store program instructions that are loadable andexecutable on the processor(s) 216, as well as data generated during theexecution of these programs. Depending on the configuration and type ofuser device 204, the memory 214 may be volatile (such as random accessmemory (RAM)) and/or non-volatile (such as read-only memory (ROM), flashmemory, etc.). The user device 204 may also include additional removablestorage and/or non-removable storage including, but not limited to,magnetic storage, optical disks, and/or tape storage. The disk drivesand their associated computer-readable media may provide non-volatilestorage of computer-readable instructions, data structures, programmodules, and other data for the computing devices. In someimplementations, the memory 214 may include multiple different types ofmemory, such as static random access memory (SRAM), dynamic randomaccess memory (DRAM), or ROM.

Turning to the contents of the memory 214 in more detail, the memory 214may include an operating system and one or more application programs orservices for implementing the features disclosed herein, such as via thebrowser application 206 or dedicated applications (e.g., smart phoneapplications, tablet applications, etc.). The browser application 206may be configured to receive, store, and/or display a website or otherinterface for interacting with the content analysis computers 210.Additionally, the memory 214 may store access credentials and/or otheruser information such as, but not limited to, user IDs, passwords,and/or other user information. In some examples, the user informationmay include information for authenticating an account access requestsuch as, but not limited to, a device ID, a cookie, an IP address, alocation, or the like. In addition, the user information may include auser 202 provided response to a security question or a geographiclocation obtained by the user device 204.

In some aspects, the content analysis computers 210 may also be any typeof computing devices such as, but not limited to, a mobile phone, asmart phone, a personal digital assistant (PDA), a laptop computer, adesktop computer, a server computer, a thin-client device, a tablet PC,etc. Additionally, it should be noted that in some embodiments, theservice provider computers are executed by one more virtual machinesimplemented in a hosted computing environment. The hosted computingenvironment may include one or more rapidly provisioned and releasedcomputing resources, which computing resources may include computing,networking and/or storage devices. A hosted computing environment mayalso be referred to as a cloud computing environment. In some examples,the content analysis computers 210 may be in communication with the userdevices 204 and/or other service providers via the networks 208, or viaother network connections. The content analysis computers 210 mayinclude one or more servers, perhaps arranged in a cluster, as a serverfarm, or as individual servers not associated with one another. Theseservers may be configured to implement the content performancemanagement described herein as part of an integrated, distributedcomputing environment.

In one illustrative configuration, the content analysis computers 210may include at least one memory 218 and one or more processing units (orprocessor(s)) 224. The processor(s) 224 may be implemented asappropriate in hardware, computer-executable instructions, firmware, orcombinations thereof. Computer-executable instruction or firmwareimplementations of the processor(s) 224 may include computer-executableor machine-executable instructions written in any suitable programminglanguage to perform the various functions described.

The memory 218 may store program instructions that are loadable andexecutable on the processor(s) 224, as well as data generated during theexecution of these programs. Depending on the configuration and type ofcontent analysis computers 210, the memory 218 may be volatile (such asRAM) and/or non-volatile (such as ROM, flash memory, etc.). The contentanalysis computers 210 or servers may also include additional storage226, which may include removable storage and/or non-removable storage.The additional storage 226 may include, but is not limited to, magneticstorage, optical disks and/or tape storage. The disk drives and theirassociated computer-readable media may provide non-volatile storage ofcomputer-readable instructions, data structures, program modules andother data for the computing devices. In some implementations, thememory 218 may include multiple different types of memory, such as SRAM,DRAM, or ROM.

The memory 218, the additional storage 226, both removable andnon-removable, are all examples of computer-readable storage media. Forexample, computer-readable storage media may include volatile ornon-volatile, removable or non-removable media implemented in any methodor technology for storage of information such as computer-readableinstructions, data structures, program modules, or other data. Thememory 218 and the additional storage 226 are all examples of computerstorage media. Additional types of computer storage media that may bepresent in the content analysis computers 210 may include, but are notlimited to, PRAM, SRAM, DRAM, RAM, ROM, EEPROM, flash memory or othermemory technology, CD-ROM, DVD or other optical storage, magneticcassettes, magnetic tape, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to store thedesired information and which can be accessed by the content analysiscomputers 210. Combinations of any of the above should also be includedwithin the scope of computer-readable media.

Alternatively, computer-readable communication media may includecomputer-readable instructions, program modules, or other datatransmitted within a data signal, such as a carrier wave, or othertransmission. However, as used herein, computer-readable storage mediadoes not include computer-readable communication media.

The content analysis computers 210 may also contain communicationsconnection(s) 228 that allow the content analysis computers 210 tocommunicate with a stored database, another computing device or server,user terminals and/or other devices on the networks 208. The contentanalysis computers 210 may also include I/O device(s) 230, such as akeyboard, a mouse, a pen, a voice input device, a touch input device, adisplay, speakers, a printer, etc.

Turning to the contents of the memory 218 in more detail, the memory 218may include an operating system 232, one or more data stores 234, and/orone or more application programs or services for implementing thefeatures disclosed herein including a text module 236, a character graphmodule 238, an actions module 240, story complexity module 242,vocabulary module 244, and/or a notification module 246. The modules maybe software modules, hardware modules, or a combination thereof. If themodules are software modules, the modules will be embodied on a computerreadable medium and processed by a processor in any of computer systemsdescribed herein. In some examples, the text module 236 may beconfigured to determine at least a portion of a text and/or text string,correlate an electronic book, movie, or other media with the textstring, determine one or more linguistic elements (e.g., verbs, nouns,or pronouns, etc.) with the text string, calculate a number of actionsand/or characters in a text string, analyze a text string using naturallanguage processing (NLP), and determine a scene in an electronic book.

The character graph module 238 may be configured to generate a charactergraph and/or analyze the character graph to help determine a maturitylevel of a text string. In some examples, the character graph module 238may not generate a character graph, but may analyze the text string todetermine the maturity level for the text string in other ways (e.g.,through the use of a dictionary or data store of mature vocabulary, bycalculating a number of occurrences of a noun, verbs, or otherlinguistic elements, etc.).

The actions module 240 may be configured to determine one or moreactions associated with a text string (e.g., via the text module 236).For example, the actions may be identified from the text string, acharacter graph (e.g., via the character graph module 238), a datastore, or other sources. The actions module 240 may also be configuredto help determine a maturity level for a text string, such that whenviolence and concepts of death are present in the text string, thematurity level is greater than when these actions are not in the textstring.

The story complexity module 242 may be configured to determine one ormore concepts associated with a text string, including a story line,scene, and linear or non-linear story flow. The story complexity module242 may also be configured to help determine a maturity level associatedwith the number of characters (e.g., via the text module 236 and/orcharacter graph module 238), such that a greater number of charactersmay also increase the maturity level of the text string.

The vocabulary module 244 may be configured to determine one or moreunique linguistic terms or words in a text string, compare linguisticelements (e.g., via the text module 236) with a dictionary or data storeof mature vocabulary, and help identify a maturity level based in parton the linguistic elements used in the text string (e.g., more complexwords may correlate to a greater maturity level than simple words,etc.). In some examples, the vocabulary module 244 may generate thedictionary (e.g., by comparing linguistic elements from other textstrings with known associations to a maturity level, by addinglinguistic elements from other text strings to the dictionary, and/or byincorporating other dictionaries with the dictionary used with thesystem, etc.).

The notification module 246 may be configured to send or receive one ormore communications from the user, including communications regardingthe maturity level for an electronic book or text, search results inresponse to a request for a recommendation for a text string with acertain maturity level, or other information.

FIG. 3 illustrates an example flow diagram for determining a maturitylevel of a text string described herein, according to at least oneexample. In some examples, the one or more content analysis computers210 (e.g., utilizing at least one of the text module 236, the charactergraph module 238, the actions module 240, the story complexity module242, the vocabulary module 244, and/or the notification module 246) orone or more user devices 204 shown in FIG. 2 may perform the process 300of FIG. 3. The process 300 may begin at 302 by determining at least aportion of a text string. For example, the content analysis computers210 may determine, receive, analyze, reference, or otherwise access atext string. The text string may be associated with an electronic bookor other media (e.g., the entire electronic book or some portion of theelectronic book, etc.).

At 304, one or more linguistic elements (e.g., verbs, nouns, pronouns,etc.) may be determined. For example, the content analysis computers 210may parse the text string to determine the one or more verbs, nouns,and/or pronouns in the text string. In some examples, the contentanalysis computers 210 may analyze the text string using naturallanguage processing to determine the one or more verbs/actions,nouns/characters, etc. in the text string. The nouns may be associatedwith one or more characters in the text string and the verbs may beassociated with actions of those characters.

At 306, a character graph may optionally be generated. For example, thecontent analysis computers 210 may generate the character graph thatassociates the one or more verbs with the one or more nouns. Thecharacter graph may associate the one or more verbs with the one or morenouns identified as character in the electronic book.

At 308, linguistic elements (e.g., verbs) may optionally be comparedwith a dictionary. For example, the content analysis computers 210 maycompare the one or more verbs in the text string with one or more verbsincluded in a dictionary. The dictionary may include maturity levelsthat associate one or more verbs (e.g., each of the verbs, some of theverbs, etc.) with a corresponding maturity level. In some examples, thedictionary may be implemented as a data store of mature vocabulary, sothat the verbs in the data store (e.g., dictionary) identify verbsassociated with greater maturity levels.

At 310, a maturity level may be determined. For example, the maturitylevel may be based in part on the comparison of one or more verbs withthe dictionary (e.g., using the maturity level identified by thedictionary, averaging the maturity level of the identified verbs,choosing the greatest maturity level associated with the verbs in thedictionary, etc.). In some examples, the content analysis computers 210may determine a maturity level based in part on the character graph(e.g., the complexity of the character graph, the size of the charactergraph, etc.).

FIG. 4 illustrates some examples of determining a maturity level of atext string described herein, according to at least one example. Inillustration 400, a computing device 402 interacts with a data store 404and/or a text string associated with an electronic book 406. Examples ofthe computing device 402 and data store 404 are illustrated in FIG. 2 asthe content analysis computers 210 and data store 234, respectively.

The electronic book 406 may include a title, author, chapter(s),scene(s), and other aspects of a book. These portions of the book may bepartitioned into a text string. For example, the text string maycomprise the entire book, title of the book, one or more chapters of thebook, one or more scenes (e.g., of a play, script, subtitles, electronictext, etc.), or any other collection of terms that are associated withthe electronic book (e.g., epilogue, prologue, forward, preface,summary, brief description, footnotes, etc.). In another example, thecomputing device 402 may analyze a single chapter of the electronic bookas the text string (e.g., in order to extrapolate the overall maturitylevel of the book from that one chapter). In some examples, thecomputing device 402 may analyze a number of terms or linguisticelements within a threshold distance of each other (e.g., include the100 terms before and 100 terms after the word “conversation,” etc.) or athreshold number of terms or linguistic elements as the text string(e.g., about 1,000 words in the middle of the text, etc.).

In some examples, the electronic book 406 may correspond with a formatother than a book. For example, the electronic book 406 may be a spokenstory. The electronic book 406 may be a motion picture and the textstring may be associated with a script or subtitles of the motionpicture. In another example, the story may be associated with anelectronic book and the text string may be a passage or chapter of theelectronic book. Other stories and/or texts may be implemented as anelectronic book as well.

The computing device 402 may analyze the text string using NaturalLanguage Processing (NLP) (e.g., language as spoken or written byhumans, etc.). For example, a text string can be processed to identifydifferent linguistic elements, characters (e.g., nouns, pronouns, etc.),actions (e.g., verbs, etc.), and relationships among the charactersbeing described. One or more NLP methods may be implemented. Forexample, the computing device 402 may identify a part of speech (e.g.,to determine nouns, verbs, adjectives, etc.). In another example, thecomputing device 402 may implement phrase detection or chunking ofportions of the text string. Phrase detection or chunking may determineuseful or connected sub-parts of a text string. For example, the textstring “I went to college to submit my thesis today” may include “I wentto college” and “to submit my thesis” as separate chunks of the textstring. In some examples, the NLP may identify one or more linguisticelements.

In some examples, the computing device 402 may also or alternativelyimplement dependency parsing to help to determine relationships betweendifferent characters of the sentence. For example, the text string mayinclude “John played basketball with Bob.” The relationship betweenJohn-Basketball and John-Bob may be determined through a parse tree orgraph generated using dependency parsing. Once the parse tree or graphis created using these techniques, the parse tree or graph can be prunedto remove noise (e.g., unnecessary parts when analyzing the naturallanguage of the text string). For example, when a character is mentionedonce or twice in a story, the character may be pruned or ignored. Inanother example, when the character performs few actions (e.g.,associated with a few number of verbs or other linguistic elements), thecharacter may be pruned or ignored. In some examples, the pruning mayhelp highlighting prominent subsets of characters, reduce the overallcomplexity of the graph, and/or improve accuracy of the NLP.

Nouns, pronouns, and characters 410 may be determined from theelectronic book 406. For example, the computing device 402 may identifythe characters 410 by comparing known character names in a dictionarywith a term identified in the text string. In another example, thecapitalization or case of the term may be identified as a character bythe computing device 402 (e.g., “Yesterday, May walked down the street”versus “I may walk down the street”). In another example, the sentencestructure may be analyzed to determine the character (e.g., in relationto linguistic elements including a verb or adjective, etc.). Asillustrated, the characters 410 may include “John,” “Sally,” “Jane,” and“Bob” for a particular electronic book 406.

Verbs, prepositions with verbs, and/or actions 412 may be determinedfrom the text as well. For example, the computing device 402 mayidentify the actions 412 by comparing known actions in a dictionary witha term identified in the text string. In another example, theconjugation of the term in one or more parts of the text string may beidentified as a verb by the computing device 402 (e.g., “John walks downthe street” versus “John walked down the street” versus “John is walkingdown the street” to identify “walk” as a verb). In another example, thesentence structure may be analyzed to determine the action (e.g., inrelation to a noun or pronoun, etc.). As illustrated, the actions 412may include “speaks,” “runs with,” “talks to,” and “throws a ball to”for a particular electronic book 406 and/or text string associated withthe electronic book 406.

Scenes 414 may be determined from a text string as well. For example,the computing device 402 may identify a scene between two headings(e.g., “chapter one” and “chapter two”) or a delineation of a scene in ascript (e.g., that mentions the term “scene” or other identifier). Insome examples, the scene corresponds with a paragraph or other thresholdnumber of terms (e.g., a minimum number of 100 terms, a minimum of tenactions, etc.). As illustrated, the scene may include one or moresentences, including “John runs with Bob,” “John talks to Jane,” and“Bob, Jane, and Tom throw a ball to Sally.”

In some examples, a scene may be determined by calculating a number ofnouns associated with the scene and comparing the number of nouns with acharacter threshold. The character threshold may be associated with oneor more maturity levels. For example, a character threshold for a firstgrade maturity level may be two characters while a character thresholdfor a third grade book may be four characters. In some examples, thegreater number of nouns corresponds with a greater maturity level. Inother examples, a single character threshold may be used, such that whenthe number of characters exceeds four, the maturity level is that of atleast a seven year old, native English speaker. The character thresholdand the number of nouns associated with the scene may be compared inorder to determine the maturity level of the electronic book.

In some examples, an importance of a character may be identified aswell. For example, the computing device 402 may implement naturallanguage processing (NLP) and/or dependency parsing to create a graph.The characters that remain in the draft may be considered an “important”character. In another example, the characters that are mentioned lessfrequently (e.g., below a threshold) may be pruned from the graph. Insome examples, the characters that are mentioned above a thresholdnumber (e.g., 10, 100, etc.) of times may be considered an “important”character.

FIG. 5 illustrates some examples of determining a maturity level of atext string described herein, according to at least one example. Inillustration 500, a computing device 502 interacts with a data store 504and/or a text string associated with an electronic book 506. Examples ofthe computing device 502 and data store 504 are illustrated in FIG. 2 asthe content analysis computers 210 and data store 234, respectively.

The electronic book may be compared with a dictionary 510. Thedictionary may include one or more linguistic elements and/or actions(e.g., verbs, verbs with prepositions, etc.) and a correspondingmaturity level. The computing device 502 may determine the maturitylevel of the book based in part on the comparison of the one or moreverbs with the maturity level of the one or more verbs in the dictionaryof verbs. The dictionary may include other actions, verbs, or terms withan associated maturity level for that term. As illustrated, the action“speaks with” corresponds with the maturity level “first grade,” theaction “converses” corresponds with the maturity level “second grade,”the action “schmoozes” corresponds with the maturity level “thirdgrade,” and the action “exchanges” (e.g., in terms of speech)corresponds with the maturity level “third grade.”

The action may be identified in the text string. For example, afterdetermining the text string 512 in the electronic book 506, thecomputing device 502 may parse the text string to identify one or moreactions in the text string. As illustrated, the action 514 “converses”is identified in the text string. The computing device 502 may comparethe action 514 with the previously identified actions in the dictionary510 to determine whether the action appears in the dictionary and/ordetermine additional information about the action from the dictionary.

Some types of information that may be determined from the dictionary isa maturity level. In some examples, when a term appears in the textstring, the computing device 502 may identify or otherwise parse thatterm, compare it with the dictionary, and if the term is included withthe dictionary, associate at least a portion of the text string withthat particular maturity level. The maturity level of the text stringmay be based in part on a comparison of one or more verbs with adictionary of verbs. As illustrated, the action “converses” isidentified in the dictionary 510 as associated with a maturity level of“second grade.”

As another example illustration, a maturity level of a text string mayinclude “6 year-old English speaker” (e.g., first grade). The verbsassociated with this text may include relatively simple verbs, including“talk,” “speak,” “run,” “play,” “throw,” “eat,” “go,” “send,” and thelike. Comparatively, a maturity level of a text may include “8 year-oldEnglish speaker” (e.g., third grade). The verbs associated with thistext may include more complex verbs, including “traverse,” “schmooze,”“exchange,” and the like. In some examples, the maturity level isassociated with a number of years of speaking English (e.g., languageproficiency) instead of a maturity or age of the user, which may berelevant for non-native English speakers.

In some examples, the dictionary may identify a topic of the linguisticelement (e.g., verb). For example, the actions “speaks with,”“converses,” “schmoozes,” and “exchanges” may all correlate with speechor communication. The various forms of communication may correspond withdifferent maturity levels, based in part on the difficulty ofpronouncing or spelling the word, or other objective measurementsassociated with a term's complexity. The computing device 502 may adjustthe maturity level of the electronic book to a greater maturity levelbased in part on the association of the one or more verbs when thedifficulty of pronouncing or spelling the verb similarly increases. Insome examples, the computing device 502 may adjust the maturity level ofthe electronic book to a greater maturity level based in part on thenumber of unique linguistic terms in the text string exceeding athreshold.

Another topic of a linguistic element (e.g., verb) is violence and/ordeath, which may also affect the maturity level of the text string. Forexample, the computing device 502 may determine that one or more verbsare associated with violence (e.g., by comparing them to a dictionary,by identifying a synonym of the verb that is associated with violence,etc.). The computing device 502 may adjust the maturity level of theelectronic book to a greater maturity level based in part on theassociation of the one or more verbs with violence.

The dictionary may be generated using various methods. For example, thecomputing device 502 may receive a different electronic book thatincludes one or more actions/verbs and a particular maturity level(e.g., for the entire book, for the particular verb, etc.). Thecomputing device 502 may identify verbs in the different electronic bookand update the dictionary to include the verbs. In some examples, thedictionary includes a link or reference to the different electronic bookwithout copying the verb.

In some examples, the different electronic book is associated with aparticular maturity level. When the one or more linguistic elements areidentified in the different electronic book, the maturity levelassociated with the different electronic book may also be associatedwith the linguistic elements. In some examples, only some of thelinguistic elements are associated with the overall maturity level ofthe book. After the maturity level and/or linguistic elements areidentified, the dictionary may be updated to include at least onelinguistic element associated with the particular maturity level (e.g.,from the different electronic book).

The dictionary may be generated using terms from other sources. Forexample, the computing device 502 may update the dictionary with one ormore linguistic elements and a user may associate the maturity level ofthose linguistic elements in the dictionary. In some examples, thecomputing device 502 may analyze an electronic book associated with aknown maturity level and associate one or more verbs or nouns (or otherlinguistic elements) in that electronic book as verbs or nouns torecognize in other electronic books. As an illustration, a first gradereading list of electronic books may be identified as corresponding witha first grade maturity level. The computing device 502 may parse theelectronic books listed in the reading list, receive a text stringassociated with each of the electronic books, determine the nouns/verbsassociated with the text string(s), and update the dictionary to includethose verbs/nouns and the first grade maturity level.

In some examples, a source of words and synonyms is analyzed to generateor supplement the dictionary. For example, a lexical data store thatincludes various linguistic elements, including nouns, verbs,adjectives, or adverbs that are grouped into sets of cognitive synonymsor concepts may be used. In other examples, a tool providingcomputational linguistics or natural language processing (NLP) may alsobe used to help generate the dictionary.

The dictionary may be generated based in part on actions of a user. Forexample, one or more users may be associated with a particular maturitylevel. The verbs and other linguistic elements that the user uses whilesending communications (e.g., text or SMS messages, emails, etc.),searching for items (e.g., through a search tool on a web page orelectronic marketplace), reading other electronic books (e.g., the usersearches and/or reads a news article or a public book), or other sourcesmay be associated with the user's maturity level. In some examples, theuser may be associated with a maturity level through a profile, browsinghistory, or other source of information that can determine the user'sreading, maturity level, proficiency of the English language, or age.

The more advanced maturity level may also be included with more complexconcepts, including violence (e.g., “shoot” or “killed”) andaffection/marriage (e.g., “kissed”). In an example illustration, acharacter “John” in a story may shoot two other characters in the story,“Jim” and “Jane.” The term “shoot” may be associated with an advancedmaturity level (e.g., fourth grade), so that whenever this actionappears in the text string, the maturity level of the text starts at aminimum of fourth grade. In this illustration, the term appears at leasttwice (e.g., for each Jim and Jane), causing the overall maturity levelof the text string to equal at least a fourth grade maturity level. Thefrequency of using the action “shoot” (or its equivalents) in the textstring may increase the maturity level of the text string to fifthgrade. In some examples, the term (or synonyms of the term) may beassociated with a data store for mature vocabulary.

In some examples, the complexity of the linguistic element maycorrespond with a maturity level of the user instead of an age. Forexample, the term “shoot” may correspond with a second year Englishspeaker and the terms “annihilate,” “catapult,” or “vaporize” maycorrespond with a fourth year English speaker, even though the terms maybe synonyms or associated with similar concepts. In some examples, oneor more of these linguistic elements may also be associated with a datastore for mature vocabulary.

In some examples, a violence rating may be determined for the linguisticelement. The violence rating may correspond with a particular characteror other linguistic element in a portion of the text string. Forexample, the term “shoot” may correspond with a relatively low violencerating (e.g., 3 out of 10) whereas “disfigure” or “maim” may correspondwith a higher violence rating (e.g., 7 out of 10). When the linguisticelement associated with the higher violence rating is associated withthe character and/or text string, the maturity level of the characterand/or text string may also be greater. The computing device 502 maydetermine that a particular character is violent based in part theassociation of the character with violent verbs, concepts, or a greaterviolence rating.

A hierarchy of linguistic elements may be considered. For example, abase level of the verb “shoot” may correspond with a minimum maturitylevel for the concept of violence, including a second grade maturitylevel. The term “annihilate” may be associated with a greater maturitylevel, based in part on the violent concept of the verb and also thecomplexity of the verb. The maturity level of the more complex verbassociated with the mature concept may be a third or fourth gradematurity level, or the like.

In some examples, the maturity level may be associated with a particularconcept in the text string. Using the concept of “death” as an exampleillustration, the concept of death may be expressed in the text withoutusing terms directly related to death, including “John is met by theGrim Reaper” and “John stops moving and Jane cries.” The computingdevice 502 may determine the concept of death discussed in the textstring and correlate the text string with a particular maturity levelfor that concept, even though the terms “death,” “die,” or “kill” arenot included with the text string. The computing device 502 may identifythe concept of death and adjust the maturity level corresponding to themore mature concept (e.g., changing the maturity level from first gradeto third grade) and/or mature vocabulary.

In some examples, the maturity level of the text string may be based inpart on a tense of one or more verbs for a particular character. Forexamples, the computing device 502 may determine a tense of one or moreverbs for a particular character in the electronic book (e.g.,describing a character in the present tense while the character is stillalive in the story). The computing device 502 may also determine thatthe tense of the one or more verbs changes from present tense to pasttense for the particular character (e.g., describing the character asdeceased and referring to the characters actions in the past when thecharacter was still alive). The maturity level may be adjusted to agreater maturity level based in part on the change in tense.

In some examples, actions may be determined based upon other factors inthe text, such as the tense of the verbs or the determination that acharacter is no longer referenced. In an example illustration, thebeginning of the text string may correlate character John with presenttense verbs and the middle to the end of the text string may correlatecharacter John with past tense verbs (e.g., to identify the death ofcharacter John). In other examples, reference to character John may notcorrespond with a flow of the story and/or text string, as identified bythe computing device 502. In other examples, character John may not bepresent in the story after a particular point, which can identify thatcharacter John may have been killed, thus associating the text with agreater maturity level.

FIG. 6 illustrates some examples of determining a maturity level of atext string described herein, according to at least one example. Forexample, for an electronic book, the content analysis computers 210 maygenerate a character graph 600 associated with at least a portion of atext string in the electronic book. The character graph comprise one ormore linguistic elements, including one or more characters in the textstring represented as nodes of the character graph and one or more verbsin the text string represented as edges of the character graph based atleast in part on the one or more verbs being associated with the one ormore characters in the text string. In some examples, one or morelinguistic elements from the text string may be represented as a node oredge of the character graph.

In character graph 600, a noun 602 may be determined in a text stringand included with the character graph. In a node and edge format, theverbs 604 or actions associated with the noun may branch off of thenoun. As illustrated, Bob is a noun represented as a node and theactions performed by or associated with Bob are identified as edges orlines from the node corresponding with character “Bob.” In the textstring, Bob may “rhyme,” “attend,” “offer,” “speak,” and “converse.”Other formats of a character graph may be implemented without divertingfrom the scope of the disclosure.

In some examples, the one or more verbs in the character graph areassociated with one or more weights 606. For example, the one or moreweights may be associated with a complexity of the corresponding verbs.The more complex verbs (e.g., associated with a greater maturity level)may affect the overall maturity level more than a relatively simpleverb. For example, the verb “speaks” may be associated with a lesserweight in comparison to a more complex verb like “converses” with agreater weight. When “converses” appears in the text string, the weightof the complex term may affect the overall maturity level (e.g.,increasing the overall maturity level to the level associated with“converses” instead of the maturity level associated with “speaks”). Insome examples, the content analysis computers 210 or other computingdevice may determine the maturity level of the electronic book based inpart on the weights associated with the one or more verbs.

The weights 606 of the character graph may identify other metrics aswell. For example, the weights may be affected by the frequency of theverb and/or concept associated with the verb in the story. For example,when the term “shoot” appears in the text string frequently (or otherterms associated with violence), the maturity level of the text may begreater than a text string with a relatively infrequent use of violentterms and/or no violent terms. In some examples, the use of the violentterm may correspond with a greater weight, thus having a greater effecton the overall maturity level.

Verbs may be combined as well, and identified through the use of theweights 606. For example, when two characters speak to each otherfrequently, the edges of the character graph may be combined andassociated with a greater weight than two characters that infrequentlyspeak to each other. The edges of the character graph, in some examples,may be unique and weights may identify the verbs with higher importanceor effect on the maturity level of the text string. In other examples,weights may correspond with a particular verb and the frequency of theuse of the verb in the text string.

In some examples, the noun 602 may be associated with other nouns 608and/or verbs 604. As illustrated, character Bob speaks with characterJane in the text string, so characters Bob and Jane are nodes in thecharacter graph with a connection (e.g., edge, line, etc.) between thetwo nodes showing their interaction. The character (e.g., Bob) mayinteract with other characters (e.g., John, Jim) as well. The number ofother characters that interact with Bob may be determined as well (e.g.,in order to determine a greater story complexity).

The composition of the character graph may affect the maturity level. Insome examples, the composition of the character graph may include thenumber of nodes and/or edges in the character graph. For example, acharacter graph with three nodes and one-hundred edges may be associatedwith a more complex story and/or text string than a character graph withtwo nodes and twenty edges.

In some examples, the composition of the character graph may be comparedwith one or more thresholds to determine the maturity level. Forexample, the number of nodes may be compared with a character thresholdand/or the number of edges may be compared with an action threshold toidentify the maturity level (e.g., to identify the complexity). As anillustration, a first grade maturity level may include a minimumthreshold of two nodes and/or twenty edges, so that when the textexceeds one or both thresholds, the text string is associated with aminimum first grade maturity level. A second grade maturity level may beassociated with a minimum character threshold of four nodes and/or fiftyedges. A fourth grade maturity level may be associated with a minimumcharacter threshold of fifteen nodes and/or seventy-five edges, and thelike. If the text fails to exceed a character threshold, actionthreshold, or both thresholds, the text string may remain at a lowermaturity level (e.g., first grade).

The complexity of the character graph may affect the overall maturitylevel. For example, the content analysis computers 210 may determinethat the complexity of the character graph increases when a number ofnodes or edges of the character graph increase. In another example, thecontent analysis computers 210 may determine that the complexity of thecharacter graph increases when one or more verbs associated withviolence or death increases.

The overall maturity level of the text string may be determined based inpart on one or more of the character graph, vocabulary, the comparisonof the one or more linguistic elements with the dictionary, thecomplexity of the character graph represented as nodes and edges, orother analysis discussed herein. These sources of the analysis may beweighted. For example, a character graph showing story a high complexity(e.g., a large number of characters in comparison to a characterthreshold) and violent actions may have more weight in the overallmaturity level determination than the vocabulary used in the textstring. An overall maturity level of the text string may be determined.For example, the content analysis computers 210 may determine theoverall maturity level of the text string based at least in part on thecomparison of the one or more verbs with the dictionary. The contentanalysis computers 210 may also and/or alternatively determine theoverall maturity level of the text string based at least in part on thecomplexity of the character graph represented as nodes and edges.

FIG. 7 illustrates an illustrative flow for providing information to auser based in part on the determined maturity level described herein,according to at least one example. The process 700 can begin withreceiving a request for a book at a maturity level at 702. For example,the computer system 704 can provide a network page 706 that allows auser 708 to search for an item that contains a text string. The user 708may type “first grade” into the search tool to look for books associatedwith a first grade maturity level, select “first grade” from a drop downmenu or radio button, or any other method known in the art (e.g., speakto type, check box selection, etc.). The computer system 704 mayinteract with a data store 710 to access information and about thesearch terms provided by the user 708. Examples of the computer system704 and data store 710 are provided throughout the disclosure, includingcontent analysis computers 210 and data store 234 illustrated in FIG. 2.

The process 700 may determine a book at 720. For example, the computersystem 704 may identify the maturity level requested by the user as“first grade” and query the data store 710 for any books associated withthis maturity level. The maturity levels for various books may be storedin the data store 710 based in part on a previous interaction with anelectronic book, as illustrated throughout the specification includingin FIG. 1. For example, the requested maturity level (e.g., from theuser 708) may be compared with the maturity level associated with theportion of the text string (as discussed in at least FIG. 5). When therequested maturity level is similar to the maturity level associatedwith the portion of the text string, the computer system 704 canidentify the electronic book associated with the story and/or requestedmaturity level.

Additional information may also be stored in data store 710, includinginformation associated with the text string or electronic book (e.g.,title, price, etc.). As illustrated in table 722, the items in the datastore 710 include a book titled “Big Red Ball” associated with a firstgrade maturity level, “The Subway” associated with a second gradematurity level, and “A Play Date for Bob” also associated with a firstgrade maturity level.

The process 700 may provide a reference for the book at 730. Forexample, the computer system 704 may provide a list of books thatcorrespond with the requested maturity level through a network page 732for the user 708. The network page 732 may include information about thebooks, including images, titles of the books, authors, and otherinformation. In some examples, the network page 732 may provide a toolthat allows the user to order the book through the network page 732(e.g., by accessing an electronic marketplace and/or one or more sellersthat offer the item for purchase).

The computer system 704 may provide a reference for the electronic bookassociated with the story in response to the request for the electronicbook associated with the maturity level. The reference to the electronicbook may be provided as a link to the electronic book, a network pagethat identifies the electronic book(s), rankings/filters associated withthe identification of the electronic book, or other formats of providingthe reference.

In some examples, an electronic book may be provided as the reference tothe book, instead of a link to the electronic book. For example, theuser 708 may request a book directed at a six year native Englishspeaker maturity level. The user may provide the request through anelectronic book reader or other user computing device reference inrelation to FIG. 2. The computer system 704 may determine the book(e.g., via the data store 710) and provide the text of the electronicbook directly to the electronic book reader operated by the user 708.

In some examples, the user 708 may be a third party computer and thereference for the book may be provided as a service by the computersystem 704 to the third party computer. For example, the computer system704 may receive a request for the overall maturity level of theelectronic book from a third party computer and the computer system 704can provide the overall maturity level of the electronic book to thethird party computer. The third party computer may provide links to thebooks (e.g., through the third party's catalog of content) based in parton the information provided by the computer system 704.

In another example, the third party computer may request the maturitylevel of a particular book as part of a service provided by computersystem 704. For example, the computer system 704 can receive a requestfor the overall maturity level of the electronic book from a third partycomputer and provide the overall maturity level to the third partycomputer. The third party computer may provide recommendations of thebook, rank/filter the books, or simply provide the maturity level forone or more books as a list to a user (e.g., “these are the books thatyour first grade son can read”).

Illustrative methods and systems for determining a maturity level of abook are described above. Some or all of these systems and methods may,but need not, be implemented at least partially by architectures such asthose shown at least in FIGS. 1-7 above.

FIG. 8 illustrates aspects of an example environment 800 forimplementing aspects in accordance with various embodiments. As will beappreciated, although a Web-based environment is used for purposes ofexplanation, different environments may be used, as appropriate, toimplement various embodiments. The environment includes an electronicclient device 802, which can include any appropriate device operable tosend and receive requests, messages, or information over an appropriatenetwork 804 and convey information back to a user of the device.Examples of such client devices include personal computers, cell phones,handheld messaging devices, laptop computers, set-top boxes, personaldata assistants, electronic book readers, and the like. The network caninclude any appropriate network, including an intranet, the Internet, acellular network, a local area network or any other such network orcombination thereof. Components used for such a system can depend atleast in part upon the type of network and/or environment selected.Protocols and components for communicating via such a network are wellknown and will not be discussed herein in detail. Communication over thenetwork can be enabled by wired or wireless connections and combinationsthereof. In this example, the network includes the Internet, as theenvironment includes a Web server 806 for receiving requests and servingcontent in response thereto, although for other networks an alternativedevice serving a similar purpose could be used as would be apparent toone of ordinary skill in the art.

The illustrative environment includes at least one application server808 and a data store 810. It should be understood that there can beseveral application servers, layers, or other elements, processes orcomponents, which may be chained or otherwise configured, which caninteract to perform tasks such as obtaining data from an appropriatedata store. As used herein the term “data store” refers to any device orcombination of devices capable of storing, accessing, and/or retrievingdata, which may include any combination and number of data servers,databases, data storage devices and data storage media, in any standard,distributed or clustered environment. The application server can includeany appropriate hardware and software for integrating with the datastore as needed to execute aspects of one or more applications for theclient device, handling a majority of the data access and business logicfor an application. The application server provides access controlservices in cooperation with the data store, and is able to generatecontent such as text, graphics, audio and/or video to be transferred tothe user, which may be served to the user by the Web server in the formof HTML, XML or another appropriate structured language in this example.The handling of all requests and responses, as well as the delivery ofcontent between the client device 802 and the application server 808,can be handled by the Web server. It should be understood that the Weband application servers are not required and are merely examplecomponents, as structured code discussed herein can be executed on anyappropriate device or host machine as discussed elsewhere herein.

The data store 810 can include several separate data tables, databasesor other data storage mechanisms and media for storing data relating toa particular aspect. For example, the data store illustrated includesmechanisms for storing production data 812 and user information 816,which can be used to serve content for the production side. The datastore also is shown to include a mechanism for storing log data 814,which can be used for reporting, analysis, or other such purposes. Itshould be understood that there can be many other aspects that may needto be stored in the data store, such as for page image information andto access right information, which can be stored in any of the abovelisted mechanisms as appropriate or in additional mechanisms in the datastore 810. The data store 810 is operable, through logic associatedtherewith, to receive instructions from the application server 808 andobtain, update or otherwise process data in response thereto. In oneexample, a user might submit a search request for a certain type ofitem. In this case, the data store might access the user information toverify the identity of the user, and can access the catalog detailinformation to obtain information about items of that type. Theinformation then can be returned to the user, such as in a resultslisting on a web page that the user is able to view via a browser on theuser device 802. Information for a particular item of interest can beviewed in a dedicated page or window of the browser.

Each server typically will include an operating system that providesexecutable program instructions for the general administration andoperation of that server, and typically will include a computer-readablestorage medium (e.g., a hard disk, random access memory, read onlymemory, etc.) storing instructions that, when executed by a processor ofthe server, allow the server to perform its intended functions. Suitableimplementations for the operating system and general functionality ofthe servers are known or commercially available, and are readilyimplemented by persons having ordinary skill in the art, particularly inlight of the disclosure herein.

The environment in one embodiment is a distributed computing environmentutilizing several computer systems and components that areinterconnected via communication links, using one or more computernetworks or direct connections. However, it will be appreciated by thoseof ordinary skill in the art that such a system could operate equallywell in a system having fewer or a greater number of components than areillustrated in FIG. 8. Thus, the depiction of the system 800 in FIG. 8should be taken as being illustrative in nature, and not limiting to thescope of the disclosure.

The various embodiments further can be implemented in a wide variety ofoperating environments, which in some cases can include one or more usercomputers, computing devices or processing devices which can be used tooperate any of a number of applications. User or client devices caninclude any of a number of general purpose personal computers, such asdesktop or laptop computers running a standard operating system, as wellas cellular, wireless and handheld devices running mobile software andcapable of supporting a number of networking and messaging protocols.Such a system also can include a number of workstations running any of avariety of commercially-available operating systems and other knownapplications for purposes such as development and database management.These devices also can include other electronic devices, such as dummyterminals, thin-clients, gaming systems and other devices capable ofcommunicating via a network.

Most embodiments utilize at least one network that would be familiar tothose skilled in the art for supporting communications using any of avariety of commercially-available protocols, such as TCP/IP, OSI, FTP,UPnP, NFS, CIFS, and AppleTalk®. The network can be, for example, alocal area network, a wide-area network, a virtual private network, theInternet, an intranet, an extranet, a public switched telephone network,an infrared network, a wireless network, and any combination thereof.

In embodiments utilizing a Web server, the Web server can run any of avariety of server or mid-tier applications, including HTTP servers, FTPservers, CGI servers, data servers, Java servers, and businessapplication servers. The server(s) also may be capable of executingprograms or scripts in response requests from user devices, such as byexecuting one or more Web applications that may be implemented as one ormore scripts or programs written in any programming language, such asJava®, C, Visual C#® or C++, or any scripting language, such as Perl®,Python® or TCL, as well as combinations thereof. The server(s) may alsoinclude database servers, including without limitation thosecommercially available from Oracle®, Microsoft®, Sybase®, and IBM®.

The environment can include a variety of data stores and other memoryand storage media as discussed above. These can reside in a variety oflocations, such as on a storage medium local to (and/or resident in) oneor more of the computers or remote from any or all of the computersacross the network. In a particular set of embodiments, the informationmay reside in a storage-area network (SAN) familiar to those skilled inthe art. Similarly, any necessary files for performing the functionsattributed to the computers, servers or other network devices may bestored locally and/or remotely, as appropriate. Where a system includescomputerized devices, each such device can include hardware elementsthat may be electrically coupled via a bus, the elements including, forexample, at least one central processing unit (CPU), at least one inputdevice (e.g., a mouse, keyboard, controller, touch screen or keypad),and at least one output device (e.g., a display device, printer orspeaker). Such a system may also include one or more storage devices,such as disk drives, optical storage devices, and solid-state storagedevices such as RAM or ROM, as well as removable media devices, memorycards, flash cards, etc.

Such devices also can include a computer-readable storage media reader,a communications device (e.g., a modem, a network card (wireless orwired), an infrared communication device, etc.) and working memory asdescribed above. The computer-readable storage media reader can beconnected with, or configured to receive, a computer-readable storagemedium, representing remote, local, fixed, and/or removable storagedevices as well as storage media for temporarily and/or more permanentlycontaining, storing, transmitting, and retrieving computer-readableinformation. The system and various devices also typically will includea number of software applications, modules, services or other elementslocated within at least one working memory device, including anoperating system and application programs, such as a client applicationor Web browser. It should be appreciated that alternate embodiments mayhave numerous variations from that described above. For example,customized hardware might also be used and/or particular elements mightbe implemented in hardware, software (including portable software, suchas applets) or both. Further, connection to other computing devices suchas network input/output devices may be employed.

Storage media and computer-readable media for containing code, orportions of code, can include any appropriate media known or used in theart, including storage media and communication media, such as but notlimited to volatile and non-volatile, removable and non-removable mediaimplemented in any method or technology for storage and/or transmissionof information such as computer-readable instructions, data structures,program modules or other data, including RAM, ROM, EEPROM, flash memoryor other memory technology, CD-ROM, DVD, or other optical storage,magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices or any other medium which can be used to storethe desired information and which can be accessed by the a systemdevice. Based on the disclosure and teachings provided herein, a personof ordinary skill in the art will appreciate other ways and/or methodsto implement the various embodiments.

The specification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense. It will, however, beevident that various modifications and changes may be made thereuntowithout departing from the broader spirit and scope of the disclosure asset forth in the claims.

Other variations are within the spirit of the present disclosure. Thus,while the disclosed techniques are susceptible to various modificationsand alternative constructions, certain illustrated embodiments thereofare shown in the drawings and have been described above in detail. Itshould be understood, however, that there is no intention to limit thedisclosure to the specific form or forms disclosed, but on the contrary,the intention is to cover all modifications, alternative constructionsand equivalents falling within the spirit and scope of the disclosure,as defined in the appended claims.

The use of the terms “a” and “an” and “the” and similar referents in thecontext of describing the disclosed embodiments (especially in thecontext of the following claims) are to be construed to cover both thesingular and the plural, unless otherwise indicated herein or clearlycontradicted by context. The terms “comprising,” “having,” “including,”and “containing” are to be construed as open-ended terms (i.e., meaning“including, but not limited to,”) unless otherwise noted. The term“connected” is to be construed as partly or wholly contained within,attached to, or joined together, even if there is something intervening.Recitation of ranges of values herein are merely intended to serve as ashorthand method of referring individually to each separate valuefalling within the range, unless otherwise indicated herein, and eachseparate value is incorporated into the specification as if it wereindividually recited herein. All methods described herein can beperformed in any suitable order unless otherwise indicated herein orotherwise clearly contradicted by context. The use of any and allexamples, or exemplary language (e.g., “such as”) provided herein, isintended merely to better illuminate embodiments of the disclosure anddoes not pose a limitation on the scope of the disclosure unlessotherwise claimed. No language in the specification should be construedas indicating any non-claimed element as essential to the practice ofthe disclosure.

Disjunctive language such as that included in the phrase “at least oneof X, Y, or Z,” unless specifically stated otherwise, is otherwiseunderstood within the context as used in general to present that anitem, term, etc., may be either X, Y, or Z, or any combination thereof(e.g., X, Y, and/or Z). Thus, such disjunctive language is not generallyintended to, and should not, imply that certain embodiments require atleast one of X, at least one of Y, and/or at least one of Z in order foreach to be present.

Preferred embodiments of this disclosure are described herein, includingthe best mode known to the inventors for carrying out the disclosure.Variations of those preferred embodiments may become apparent to thoseof ordinary skill in the art upon reading the foregoing description. Theinventors expect skilled artisans to employ such variations asappropriate, and the inventors intend for the disclosure to be practicedotherwise than as specifically described herein. Accordingly, thisdisclosure includes all modifications and equivalents of the subjectmatter recited in the claims appended hereto as permitted by applicablelaw. Moreover, any combination of the above-described elements in allpossible variations thereof is encompassed by the disclosure unlessotherwise indicated herein or otherwise clearly contradicted by context.

All references, including publications, patent applications, andpatents, cited herein are hereby incorporated by reference to the sameextent as if each reference were individually and specifically indicatedto be incorporated by reference and were set forth in its entiretyherein.

What is claimed is:
 1. A computing device comprising: a memoryconfigured to store computer-executable instructions; and a processor incommunication with the memory configured to execute thecomputer-executable instructions to at least: determine, by thecomputing device, at least a portion of a text string associated with astory; determine, by the computing device, a linguistic element in thetext string; analyze, by the computing device, at least one action inthe text string by comparing the linguistic element in the text stringto a story complexity that comprises an identification of a linear ornon-linear story flow; and determine, by the computing device, amaturity level associated with the portion of the text string based onthe analysis by at least: determining a violence rating for a particularcharacter in the portion of the text string; and adjusting the maturitylevel of the portion of the text string to a greater maturity levelbased in part on the violence rating for the particular character in theportion of the text string.
 2. The computing device of claim 1, whereinthe analysis further comprises comparing the linguistic element with adata store of mature vocabulary.
 3. The computing device of claim 1,wherein the story complexity comprises an identification of a number ofcharacters and wherein determining the maturity level associated withthe portion of the text string comprises: adjusting the maturity levelof the portion of the text string to a greater maturity level based inpart on the identification of the number of characters.
 4. The computingdevice of claim 1, wherein the analysis further comprises generating acharacter graph that represents relationships between one or morecharacters in the text string based at least in part upon one or moreverbs and the at least one action, the one or more charactersrepresented as nodes in the character graph.
 5. The computing device ofclaim 1, wherein the computer-executable instructions further comprise:receive a requested maturity level for an electronic book; compare therequested maturity level for the electronic book with the maturity levelassociated with the portion of the text string; when the requestedmaturity level for the electronic book is similar to the maturity levelassociated with the portion of the text string, identify the electronicbook associated with the story; and provide a reference for theelectronic book associated with the story in response to the requestedmaturity level for the electronic book.
 6. The computing device of claim1, wherein analyzing the story complexity further comprises: determininga character in the story; determining a number of other characters thatinteract with the character; and identifying a greater story complexitywhen the number of other characters associated with the characterexceeds a character threshold.
 7. The computing device of claim 6,wherein an interaction between the character and at least one of theother character involves a communication.
 8. The computing device ofclaim 7, wherein the character and the at least one of the othercharacter are associated with each other when a second linguisticelement associated with the character appears within a thresholddistance of a third linguistic element associated with at least one ofthe other characters in the text string.
 9. The computing device ofclaim 1, wherein the comparison of the linguistic element to the storycomplexity comprises: determining a number of unique linguistic terms inthe text string; and adjusting the maturity level when the number ofunique linguistic terms in the text string exceeds a threshold.
 10. Oneor more non-transitory computer-readable storage media collectivelystoring computer-executable instructions that, when executed by one ormore computer systems, configure the one or more computer systems tocollectively perform operations comprising: determining at least aportion of a text string associated with a story; determining alinguistic element in the text string; analyzing the linguistic elementin the text string with respect to a story complexity that comprises anidentification of a linear or non-linear story flow, wherein analyzingthe story complexity further comprises: determining a character in thestory; determining a number of other characters that interact with thecharacter; and identifying a greater story complexity when the number ofother characters associated with the character exceeds a characterthreshold; and determining a maturity level associated with the portionof the text string based on the analysis.
 11. The one or morenon-transitory computer-readable storage media of claim 10, whereindetermining the maturity level associated with the portion of the textstring comprises: determining a violence rating for a particularcharacter in the portion of the text string; and adjusting the maturitylevel of the portion of the text string to a greater maturity levelbased in part on the violence rating for the particular character in theportion of the text string.
 12. The one or more non-transitorycomputer-readable storage media of claim 10, wherein the violence ratingis stored with a data store of mature vocabulary.
 13. The one or morenon-transitory computer-readable storage media of claim 10, wherein thestory is associated with a motion picture and the text string isassociated with a script or subtitles of the motion picture.
 14. The oneor more non-transitory computer-readable storage media of claim 10,wherein the story is associated with an electronic book and the textstring is associated with a passage or chapter of the electronic book.15. The one or more non-transitory computer-readable storage media ofclaim 10, wherein the operations further comprise: receiving a requestedmaturity level for an electronic book; comparing the requested maturitylevel for the electronic book with the maturity level associated withthe portion of the text string; when the requested maturity level forthe electronic book is similar to the maturity level associated with theportion of the text string, identifying the electronic book associatedwith the story; and providing a reference for the electronic bookassociated with the story in response to the requested maturity levelfor the electronic book.
 16. A computer-implemented method comprising:determining, by a computing device, at least a portion of a text stringassociated with a story; determining, by the computing device, alinguistic element in the text string; analyzing, by the computingdevice, at least one action in the text string by comparing thelinguistic element in the text string to a story complexity thatcomprises an identification of a number of characters; and determining,by the computing device, a maturity level associated with the portion ofthe text string based on the analysis, wherein a character and at leastone other character are associated with each other when a secondlinguistic element associated with the character appears within athreshold distance of a third linguistic element associated with atleast one of the other characters in the text string.
 17. Thecomputer-implemented method of claim 16, wherein analyzing the storycomplexity further comprises: identifying a greater story complexitywhen the number of characters exceeds a character threshold.
 18. Thecomputer-implemented method of claim 16, wherein determining thematurity level associated with the portion of the text string comprises:determining a violence rating for a particular character in the portionof the text string; and adjusting the maturity level of the portion ofthe text string to a greater maturity level based in part on theviolence rating for the particular character in the portion of the textstring.